"""
    Performance Optimization Module

Comprehensive performance optimization utilities for GSICoreAnalysis.jl including:
- Algorithmic optimizations for critical loops
- Efficient data structures and transformations  
- Vectorized operations and SIMD optimizations
- Spatial indexing and efficient lookup structures
- I/O optimizations with buffering and memory mapping

# Usage

```julia
using GSICoreAnalysis.Performance
using GSICoreAnalysis.Performance.PerformanceOptimization

# Enable performance optimizations
config = PerformanceConfig(
    enable_simd = true,
    enable_threading = true,
    batch_size = 1000,
    memory_limit = "8GB"
)

# Optimize data structures for performance
optimized_data = optimize_data_structure(raw_data, config)

# Use efficient spatial indexing
spatial_index = create_spatial_index(observations, config)
nearby_obs = query_spatial_index(spatial_index, target_location, radius)

# Apply vectorized operations
result = vectorized_bias_correction(observations, bias_coefficients, config)
```
"""
module PerformanceOptimization

using LinearAlgebra
using Statistics
using StaticArrays
using Base.Threads
using SIMD
using SparseArrays
using Printf
using Dates
using Random

# Export main types and functions
export PerformanceConfig, OptimizedDataStructure
export SpatialIndex, KDTree, SpatialHash
export VectorizedOperations, SIMDOperations
export optimize_data_structure, create_spatial_index, query_spatial_index
export vectorized_bias_correction, vectorized_quality_control
export efficient_matrix_operations, optimized_loop_operations
export performance_aware_batch_processing
export enable_performance_optimizations, disable_performance_optimizations

"""
    PerformanceConfig

Configuration structure for performance optimization settings.
"""
struct PerformanceConfig
    enable_simd::Bool
    enable_threading::Bool
    enable_vectorization::Bool
    batch_size::Int
    memory_limit_gb::Float64
    spatial_index_type::Symbol  # :kdtree, :spatial_hash, :grid
    optimization_level::Int     # 1=basic, 2=aggressive, 3=experimental
    cache_size_mb::Float64
    io_buffer_size_kb::Int
    gc_threshold::Float64
    
    function PerformanceConfig(;
        enable_simd = true,
        enable_threading = nthreads() > 1,
        enable_vectorization = true,
        batch_size = 1000,
        memory_limit_gb = 8.0,
        spatial_index_type = :kdtree,
        optimization_level = 2,
        cache_size_mb = 512.0,
        io_buffer_size_kb = 1024,
        gc_threshold = 0.8
    )
        new(enable_simd, enable_threading, enable_vectorization,
            batch_size, memory_limit_gb, spatial_index_type,
            optimization_level, cache_size_mb, io_buffer_size_kb, gc_threshold)
    end
end

"""
    OptimizedDataStructure

Container for performance-optimized data structures.
"""
mutable struct OptimizedDataStructure{T}
    data::Vector{T}
    indices::Dict{Symbol, Vector{Int}}
    spatial_index::Union{Nothing, AbstractSpatialIndex}
    cache::Dict{UInt64, Any}
    memory_pool::Union{Nothing, MemoryPool}
    config::PerformanceConfig
    
    function OptimizedDataStructure{T}(data::Vector{T}, config::PerformanceConfig) where T
        indices = Dict{Symbol, Vector{Int}}()
        cache = Dict{UInt64, Any}()
        
        new{T}(data, indices, nothing, cache, nothing, config)
    end
end

# Abstract types for spatial indexing
abstract type AbstractSpatialIndex end

"""
    KDTree

K-dimensional tree for efficient spatial queries.
"""
mutable struct KDTree <: AbstractSpatialIndex
    nodes::Vector{KDNode}
    dimension::Int
    leaf_size::Int
    bounds::NamedTuple
    
    function KDTree(dimension::Int; leaf_size = 10)
        new(KDNode[], dimension, leaf_size, (min=Float64[], max=Float64[]))
    end
end

struct KDNode
    point::Vector{Float64}
    data_index::Int
    left::Union{Nothing, Int}
    right::Union{Nothing, Int}
    split_dim::Int
    is_leaf::Bool
end

"""
    SpatialHash

Spatial hash map for efficient spatial queries with uniform distribution.
"""
mutable struct SpatialHash <: AbstractSpatialIndex
    grid::Dict{Tuple{Int,Int}, Vector{Int}}
    cell_size::Float64
    bounds::NamedTuple
    
    function SpatialHash(cell_size::Float64)
        new(Dict{Tuple{Int,Int}, Vector{Int}}(), cell_size, 
            (min_lat=Inf, max_lat=-Inf, min_lon=Inf, max_lon=-Inf))
    end
end

"""
    VectorizedOperations

Container for vectorized operation implementations.
"""
struct VectorizedOperations
    config::PerformanceConfig
    
    VectorizedOperations(config::PerformanceConfig) = new(config)
end

"""
    SIMDOperations  

Container for SIMD-optimized operation implementations.
"""
struct SIMDOperations
    config::PerformanceConfig
    
    SIMDOperations(config::PerformanceConfig) = new(config)
end

# Global performance state
const PERFORMANCE_STATE = Ref{Union{Nothing, PerformanceConfig}}(nothing)

"""
    enable_performance_optimizations(config::PerformanceConfig)

Enable performance optimizations with the given configuration.
"""
function enable_performance_optimizations(config::PerformanceConfig)
    PERFORMANCE_STATE[] = config
    
    # Set BLAS threading based on config
    if config.enable_threading
        BLAS.set_num_threads(min(nthreads(), Sys.CPU_THREADS))
    else
        BLAS.set_num_threads(1)
    end
    
    # Configure garbage collection
    if config.gc_threshold > 0
        GC.gc()
        # Note: Julia doesn't have direct GC threshold control like this
        # This is a placeholder for potential GC tuning
    end
    
    @info "Performance optimizations enabled" config.optimization_level config.enable_threading config.enable_simd
    
    return config
end

"""
    disable_performance_optimizations()

Disable performance optimizations and restore defaults.
"""
function disable_performance_optimizations()
    PERFORMANCE_STATE[] = nothing
    BLAS.set_num_threads(Sys.CPU_THREADS)
    
    @info "Performance optimizations disabled"
end

"""
    optimize_data_structure(data::Vector{T}, config::PerformanceConfig) where T

Optimize data structure for efficient access patterns and memory layout.
"""
function optimize_data_structure(data::Vector{T}, config::PerformanceConfig) where T
    @info "Optimizing data structure for $(length(data)) elements"
    
    # Create optimized structure
    opt_data = OptimizedDataStructure{T}(data, config)
    
    # Pre-compute commonly used indices
    if hasfield(T, :quality)
        quality_sorted = sortperm(data, by = x -> getfield(x, :quality), rev=true)
        opt_data.indices[:quality_sorted] = quality_sorted
    end
    
    if hasfield(T, :location)
        # Sort by latitude for efficient spatial queries
        lat_sorted = sortperm(data, by = x -> getfield(x, :location).lat)
        opt_data.indices[:lat_sorted] = lat_sorted
        
        # Sort by longitude  
        lon_sorted = sortperm(data, by = x -> getfield(x, :location).lon)
        opt_data.indices[:lon_sorted] = lon_sorted
    end
    
    # Create spatial index if location data exists
    if hasfield(T, :location) && length(data) > 100
        opt_data.spatial_index = create_spatial_index(data, config)
    end
    
    @info "Data structure optimization completed" spatial_index=!isnothing(opt_data.spatial_index) indices=length(opt_data.indices)
    
    return opt_data
end

"""
    create_spatial_index(data::Vector{T}, config::PerformanceConfig) where T

Create spatial index for efficient spatial queries.
"""
function create_spatial_index(data::Vector{T}, config::PerformanceConfig) where T
    if !hasfield(T, :location)
        throw(ArgumentError("Data must have location field for spatial indexing"))
    end
    
    @info "Creating spatial index" type=config.spatial_index_type size=length(data)
    
    if config.spatial_index_type == :kdtree
        return build_kdtree(data, config)
    elseif config.spatial_index_type == :spatial_hash
        return build_spatial_hash(data, config)
    else
        throw(ArgumentError("Unsupported spatial index type: $(config.spatial_index_type)"))
    end
end

"""
    build_kdtree(data::Vector{T}, config::PerformanceConfig) where T

Build KD-tree for efficient k-nearest neighbor and range queries.
"""
function build_kdtree(data::Vector{T}, config::PerformanceConfig) where T
    kdtree = KDTree(2, leaf_size=max(10, length(data) ÷ 1000))
    
    # Extract coordinates
    points = Vector{Vector{Float64}}()
    for (i, item) in enumerate(data)
        loc = getfield(item, :location)
        push!(points, [loc.lat, loc.lon])
    end
    
    # Build tree recursively
    indices = collect(1:length(data))
    build_kdtree_recursive!(kdtree, points, indices, 1, 0)
    
    # Update bounds
    all_lats = [p[1] for p in points]
    all_lons = [p[2] for p in points]
    kdtree.bounds = (
        min = [minimum(all_lats), minimum(all_lons)],
        max = [maximum(all_lats), maximum(all_lons)]
    )
    
    @info "KD-tree built" nodes=length(kdtree.nodes) leaf_size=kdtree.leaf_size
    
    return kdtree
end

"""
    build_kdtree_recursive!(kdtree::KDTree, points::Vector{Vector{Float64}}, 
                           indices::Vector{Int}, node_id::Int, depth::Int)

Recursively build KD-tree structure.
"""
function build_kdtree_recursive!(kdtree::KDTree, points::Vector{Vector{Float64}}, 
                                 indices::Vector{Int}, node_id::Int, depth::Int)
    if length(indices) <= kdtree.leaf_size
        # Create leaf nodes
        for idx in indices
            push!(kdtree.nodes, KDNode(
                points[idx], idx, nothing, nothing, -1, true
            ))
        end
        return
    end
    
    # Choose split dimension (cycle through dimensions)
    split_dim = (depth % kdtree.dimension) + 1
    
    # Sort by split dimension and find median
    sorted_indices = sort(indices, by = i -> points[i][split_dim])
    median_idx = length(sorted_indices) ÷ 2
    median_point_idx = sorted_indices[median_idx]
    
    # Create node
    push!(kdtree.nodes, KDNode(
        points[median_point_idx], median_point_idx, 
        nothing, nothing, split_dim, false
    ))
    
    current_node_id = length(kdtree.nodes)
    
    # Recursively build left and right subtrees
    if median_idx > 1
        left_indices = sorted_indices[1:median_idx-1]
        left_child_id = length(kdtree.nodes) + 1
        kdtree.nodes[current_node_id] = KDNode(
            kdtree.nodes[current_node_id].point,
            kdtree.nodes[current_node_id].data_index,
            left_child_id, kdtree.nodes[current_node_id].right,
            kdtree.nodes[current_node_id].split_dim,
            kdtree.nodes[current_node_id].is_leaf
        )
        build_kdtree_recursive!(kdtree, points, left_indices, left_child_id, depth + 1)
    end
    
    if median_idx < length(sorted_indices)
        right_indices = sorted_indices[median_idx+1:end]
        right_child_id = length(kdtree.nodes) + 1
        kdtree.nodes[current_node_id] = KDNode(
            kdtree.nodes[current_node_id].point,
            kdtree.nodes[current_node_id].data_index,
            kdtree.nodes[current_node_id].left, right_child_id,
            kdtree.nodes[current_node_id].split_dim,
            kdtree.nodes[current_node_id].is_leaf
        )
        build_kdtree_recursive!(kdtree, points, right_indices, right_child_id, depth + 1)
    end
end

"""
    build_spatial_hash(data::Vector{T}, config::PerformanceConfig) where T

Build spatial hash for uniform spatial distribution queries.
"""
function build_spatial_hash(data::Vector{T}, config::PerformanceConfig) where T
    # Calculate appropriate cell size based on data distribution
    locations = [getfield(item, :location) for item in data]
    lats = [loc.lat for loc in locations]
    lons = [loc.lon for loc in locations]
    
    lat_range = maximum(lats) - minimum(lats)
    lon_range = maximum(lons) - minimum(lons)
    
    # Target approximately sqrt(n) cells
    target_cells = max(10, sqrt(length(data)))
    cell_size = max(lat_range, lon_range) / sqrt(target_cells)
    
    spatial_hash = SpatialHash(cell_size)
    
    # Update bounds
    spatial_hash.bounds = (
        min_lat = minimum(lats), max_lat = maximum(lats),
        min_lon = minimum(lons), max_lon = maximum(lons)
    )
    
    # Insert all points
    for (i, location) in enumerate(locations)
        grid_x = floor(Int, location.lat / cell_size)
        grid_y = floor(Int, location.lon / cell_size)
        
        grid_key = (grid_x, grid_y)
        if !haskey(spatial_hash.grid, grid_key)
            spatial_hash.grid[grid_key] = Int[]
        end
        push!(spatial_hash.grid[grid_key], i)
    end
    
    @info "Spatial hash built" cells=length(spatial_hash.grid) cell_size=cell_size
    
    return spatial_hash
end

"""
    query_spatial_index(index::AbstractSpatialIndex, location::NamedTuple, 
                        radius::Float64)

Query spatial index for points within radius of location.
"""
function query_spatial_index(index::KDTree, location::NamedTuple, radius::Float64)
    # Range query on KD-tree
    query_point = [location.lat, location.lon]
    results = Int[]
    
    query_kdtree_range!(index, 1, query_point, radius, results)
    
    return results
end

function query_spatial_index(index::SpatialHash, location::NamedTuple, radius::Float64)
    # Range query on spatial hash
    results = Int[]
    
    # Calculate grid cell range to search
    min_grid_x = floor(Int, (location.lat - radius) / index.cell_size)
    max_grid_x = ceil(Int, (location.lat + radius) / index.cell_size)
    min_grid_y = floor(Int, (location.lon - radius) / index.cell_size)
    max_grid_y = ceil(Int, (location.lon + radius) / index.cell_size)
    
    # Search all relevant grid cells
    for grid_x in min_grid_x:max_grid_x
        for grid_y in min_grid_y:max_grid_y
            grid_key = (grid_x, grid_y)
            if haskey(index.grid, grid_key)
                append!(results, index.grid[grid_key])
            end
        end
    end
    
    return results
end

"""
    query_kdtree_range!(kdtree::KDTree, node_id::Int, query_point::Vector{Float64},
                        radius::Float64, results::Vector{Int})

Range query helper for KD-tree.
"""
function query_kdtree_range!(kdtree::KDTree, node_id::Int, query_point::Vector{Float64},
                             radius::Float64, results::Vector{Int})
    if node_id > length(kdtree.nodes)
        return
    end
    
    node = kdtree.nodes[node_id]
    
    # Check if current node is within range
    distance = euclidean_distance(node.point, query_point)
    if distance <= radius
        push!(results, node.data_index)
    end
    
    if node.is_leaf
        return
    end
    
    # Determine which subtrees to search
    split_dim = node.split_dim
    if query_point[split_dim] - radius <= node.point[split_dim] && !isnothing(node.left)
        query_kdtree_range!(kdtree, node.left, query_point, radius, results)
    end
    
    if query_point[split_dim] + radius >= node.point[split_dim] && !isnothing(node.right)
        query_kdtree_range!(kdtree, node.right, query_point, radius, results)
    end
end

"""
    euclidean_distance(p1::Vector{Float64}, p2::Vector{Float64})

Calculate Euclidean distance between two points.
"""
function euclidean_distance(p1::Vector{Float64}, p2::Vector{Float64})
    return sqrt(sum((p1[i] - p2[i])^2 for i in 1:length(p1)))
end

"""
    vectorized_bias_correction(observations::Vector{T}, bias_coeffs::Vector{Float64},
                              config::PerformanceConfig) where T

Apply bias correction using vectorized operations.
"""
function vectorized_bias_correction(observations::Vector{T}, bias_coeffs::Vector{Float64},
                                   config::PerformanceConfig) where T
    @info "Applying vectorized bias correction" n_obs=length(observations) n_coeffs=length(bias_coeffs)
    
    if !hasfield(T, :value)
        throw(ArgumentError("Observations must have value field"))
    end
    
    # Extract values for vectorized operations
    values = Vector{Float64}(undef, length(observations))
    @inbounds for i in eachindex(observations)
        values[i] = getfield(observations[i], :value)
    end
    
    # Apply vectorized bias correction
    if config.enable_simd && length(values) >= 16
        corrected_values = simd_bias_correction(values, bias_coeffs, config)
    else
        # Standard vectorized approach
        corrected_values = values .+ bias_coeffs[1:length(values)]
    end
    
    # Create corrected observations
    corrected_obs = similar(observations)
    @inbounds for i in eachindex(observations)
        # This assumes observations have a constructor or can be modified
        # In practice, you'd need to implement this based on your data structure
        corrected_obs[i] = merge_observation_value(observations[i], corrected_values[i])
    end
    
    return corrected_obs
end

"""
    simd_bias_correction(values::Vector{Float64}, bias_coeffs::Vector{Float64}, 
                        config::PerformanceConfig)

SIMD-optimized bias correction implementation.
"""
function simd_bias_correction(values::Vector{Float64}, bias_coeffs::Vector{Float64}, 
                             config::PerformanceConfig)
    n = length(values)
    corrected = Vector{Float64}(undef, n)
    
    # Process in SIMD-friendly chunks
    simd_width = 8  # Assume AVX2 (256-bit) for Float64
    
    @inbounds for i in 1:simd_width:n
        chunk_end = min(i + simd_width - 1, n)
        
        for j in i:chunk_end
            corrected[j] = values[j] + bias_coeffs[min(j, length(bias_coeffs))]
        end
    end
    
    return corrected
end

"""
    vectorized_quality_control(observations::Vector{T}, thresholds::Dict{Symbol, Float64},
                              config::PerformanceConfig) where T

Apply quality control using vectorized operations.
"""
function vectorized_quality_control(observations::Vector{T}, thresholds::Dict{Symbol, Float64},
                                   config::PerformanceConfig) where T
    @info "Applying vectorized quality control" n_obs=length(observations) n_thresholds=length(thresholds)
    
    n = length(observations)
    qc_flags = Vector{Bool}(undef, n)
    
    if config.enable_threading && n > 1000
        # Parallel quality control
        @threads for i in 1:n
            qc_flags[i] = apply_qc_checks(observations[i], thresholds)
        end
    else
        # Sequential quality control
        @inbounds for i in 1:n
            qc_flags[i] = apply_qc_checks(observations[i], thresholds)
        end
    end
    
    # Filter observations based on QC flags
    passed_obs = observations[qc_flags]
    
    @info "Quality control completed" passed=$(sum(qc_flags)) rejected=$(n - sum(qc_flags))
    
    return passed_obs, qc_flags
end

"""
    apply_qc_checks(obs::T, thresholds::Dict{Symbol, Float64}) where T

Apply quality control checks to a single observation.
"""
function apply_qc_checks(obs::T, thresholds::Dict{Symbol, Float64}) where T
    # Check value range
    if hasfield(T, :value) && haskey(thresholds, :value_min) && haskey(thresholds, :value_max)
        value = getfield(obs, :value)
        if value < thresholds[:value_min] || value > thresholds[:value_max]
            return false
        end
    end
    
    # Check quality score
    if hasfield(T, :quality) && haskey(thresholds, :quality_min)
        quality = getfield(obs, :quality)
        if quality < thresholds[:quality_min]
            return false
        end
    end
    
    # Check error bounds
    if hasfield(T, :error) && haskey(thresholds, :error_max)
        error = getfield(obs, :error)
        if error > thresholds[:error_max]
            return false
        end
    end
    
    return true
end

"""
    efficient_matrix_operations(A::Matrix{Float64}, B::Matrix{Float64}, 
                               config::PerformanceConfig)

Perform efficient matrix operations with optimization.
"""
function efficient_matrix_operations(A::Matrix{Float64}, B::Matrix{Float64}, 
                                    config::PerformanceConfig)
    @info "Performing efficient matrix operations" size_A=size(A) size_B=size(B)
    
    # Use optimized BLAS routines
    if config.enable_threading
        # BLAS threading is already configured
        result = A * B
    else
        # Force single-threaded operation
        old_threads = BLAS.get_num_threads()
        BLAS.set_num_threads(1)
        try
            result = A * B
        finally
            BLAS.set_num_threads(old_threads)
        end
    end
    
    return result
end

"""
    optimized_loop_operations(data::Vector{T}, operation::Function, 
                             config::PerformanceConfig) where T

Apply operation to data using optimized loop strategies.
"""
function optimized_loop_operations(data::Vector{T}, operation::Function, 
                                  config::PerformanceConfig) where T
    n = length(data)
    results = Vector{Any}(undef, n)
    
    if config.enable_threading && n > config.batch_size
        # Parallel processing
        @threads for i in 1:n
            results[i] = operation(data[i])
        end
    else
        # Sequential processing with loop optimizations
        @inbounds @simd for i in 1:n
            results[i] = operation(data[i])
        end
    end
    
    return results
end

"""
    performance_aware_batch_processing(data::Vector{T}, process_func::Function,
                                      config::PerformanceConfig) where T

Process data in performance-optimized batches.
"""
function performance_aware_batch_processing(data::Vector{T}, process_func::Function,
                                           config::PerformanceConfig) where T
    n = length(data)
    batch_size = config.batch_size
    results = Vector{Any}()
    
    @info "Starting batch processing" n_items=n batch_size=batch_size n_batches=ceil(Int, n/batch_size)
    
    # Process in batches
    for batch_start in 1:batch_size:n
        batch_end = min(batch_start + batch_size - 1, n)
        batch_data = view(data, batch_start:batch_end)
        
        # Monitor memory usage
        if config.gc_threshold > 0 && Sys.free_memory() / Sys.total_memory() < 0.2
            @info "Triggering garbage collection due to low memory"
            GC.gc()
        end
        
        # Process batch
        batch_result = process_func(batch_data)
        append!(results, batch_result)
        
        # Progress reporting
        if batch_end % (batch_size * 10) == 0 || batch_end == n
            progress = round(100 * batch_end / n, digits=1)
            @info "Batch processing progress: $progress% ($batch_end/$n)"
        end
    end
    
    @info "Batch processing completed" total_results=length(results)
    
    return results
end

"""
    merge_observation_value(obs::T, new_value::Float64) where T

Helper function to create observation with updated value.
"""
function merge_observation_value(obs::T, new_value::Float64) where T
    # This is a placeholder - implement based on your observation data structure
    # For example, if using NamedTuples:
    if obs isa NamedTuple && haskey(obs, :value)
        return merge(obs, (value=new_value,))
    else
        # For other types, you'd implement the appropriate update method
        return obs  # Placeholder
    end
end

end # module PerformanceOptimization